code/dist_train.py

import io
import os

import torch
import torch.distributed as dist


_print = print


def get_world_size(): return int(os.getenv('WORLD_SIZE', 1))
def get_rank(): return int(os.getenv('RANK', 0))
def get_local_rank(): return int(os.getenv('LOCAL_RANK', 0))


def is_dist():
    return dist.is_available() and dist.is_initialized() and get_world_size() > 1


def print(*argc, all=False, **kwargs):
    if not is_dist():
        _print(*argc, **kwargs)
        return

    if not all and get_local_rank() != 0:
        return

    output = io.StringIO()
    kwargs['end'] = ''
    kwargs['file'] = output
    kwargs['flush'] = True
    _print(*argc, **kwargs)

    s = output.getvalue()
    output.close()

    s = '[rank {}] {}'.format(dist.get_rank(), s)
    _print(s)


def reduce_mean(tensor, nprocs=None):
    if not is_dist():
        return tensor
    if not isinstance(tensor, torch.Tensor):
        device = torch.cuda.current_device()
        rt = torch.tensor(tensor, device=device)
    else:
        rt = tensor.clone()
    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
    nprocs = nprocs if nprocs else dist.get_world_size()
    rt = rt / nprocs
    if not isinstance(tensor, torch.Tensor):
        rt = rt.item()
    return rt



def reduce_sum(tensor):
    if not is_dist():
        return tensor
    if not isinstance(tensor, torch.Tensor):
        device = torch.cuda.current_device()
        rt = torch.tensor(tensor, device=device)
    else:
        rt = tensor.clone()
    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
    if not isinstance(tensor, torch.Tensor):
        rt = rt.item()
    return rt


def barrier():
    if not is_dist():
        return
    dist.barrier()